• Data collection
• Software

Large-scale web scraping: Guide to efficient practices

Contents of article:

• Web scraping tools: the best datacenter proxies and other components of architecture
• Stack of technologies in large-scale data collection

Data scraping tasks have evolved from manual “copy–paste” operations to automated scripts, and then to complex systems. They operate AI-engaged solutions, handle gigabytes of information, and deploy thousands of the best datacenter proxies. The Dexodata ecosystem offers dynamic, HTTP(S) and SOCKS5 compatible IP addresses compatible with corporate-level info gathering projects.

Scaling data harvesting pipelines, at the same time, is not just about raising the bandwidth capacity or buying dedicated proxies at larger amounts. Cooperation with the Dexodata platform supports the ethical status of web scraping due to strict KYC and AML compliance, and it is only a single factor to consider preparing the large-scale extraction of online insights.

Web scraping tools: the best datacenter proxies and other components of architecture

The purpose of acquiring publicly available online information through HTML or API varies, while core mechanics stay the same. The performer sends a HTTP request, manually or through automated software, the target site processes it and answers — or denies providing the crucial insights. When you buy residential IP pools’ access it raises the chance of passing site’s checks on sender’s reliability and authenticity, along with user-agent controllers, browser actions’ automatons, CAPTCHA passing tools, etc.

The conjunction of tools needed to get structured databases from the URLs’ list and overcome scraping obstacles constitutes the project’s architecture. The more sites you need to obtain the info from, the more dedicated proxies you buy, and the more complex the architecture is. Scaling the info acquiring process includes:

1. Technological stack
2. Vertical or horizontal architecture
3. Load balancing, dynamic or static
4. Handling critical parts, technology-wise and business-wise.

Operating the best datacenter proxies or residential ones is crucial, yet not enough. The whole system’s sustainability depends on other components of a pipeline.

Stack of technologies in large-scale data collection

Forming the technological stack leads to choosing:

• Between Scrapy or Beautiful Soup, or applying both frameworks
• Headless browsers (Playwright, Selenium WebDriver, Zombie.js)
• Asynchronous libraries (aiohttp, axios, Jsoup, Flurl)
• Ecosystems, offering to buy residential IP addresses, dynamic within proxy pools on city and ISP levels.
• Crawling frameworks, distributing tasks (e.g. Scrapy Cluster)
• Containers (Docker, Kubernetes)
• Queue systems (RabbitMQ, Apache Kafka)
• Data processing frameworks (Pandas, NumPy, Apache Spark).

The choice of architecture depends on human resources, financial and hardware aspects. Vertical architecture engages a single machine and handles complex single-thread tasks better. Horizontal type of architecture relies on multiple computing centers, and therefore is great for large-scale operations with concurrent scraping tasks and the best datacenter proxies.

Proper selection of a web parser and supportive instruments requires additional effort on maintaining them properly through:

1. Load balancing
2. Fixing critical parts.

Load balancing relies on a prescribed order of actions (static, based) or takes into account the real-time statistics of traffic and servers’ workload (dynamic balancing). According to applied architecture, load balancing algorithms buy residential IPs and rotate their external addresses, enable cloud environments or retry mechanisms, customize HTTP headers, and so on.

Corporate scopes imply fixing critical parts of the project on two levels:

Creating a robust web data harvesting pipeline takes time and effort. To reduce costs strive for open source scraping technologies and buy dedicated proxies from $3.65 per 1 GB from Dexodata. Extract information seamlessly and scale up your business!